Novel Average Consensus Algorithm for Multi-agent Systems via Super-nodes Cooperation

We propose a novel method for multi-agent systems via super-nodes cooperation, in order to improve the convergence rate of multi-agent systems to achieve consensus. The new algorithm establishes a graph signal model for the multi-agent system, and selects super-nodes to cooperate in the graph, improve the speed of consensus. First, we select some super-nodes and divide local sets by single-hop sampling algorithm, and cooperate the nodes in the local set. Then the coarsened graph is obtained by edge connection between super-nodes, and the coefficients of the graph filter are designed by the eigenvalues of the Laplacian matrix. Finally, the signal of super-nodes are averaged by iteration of the graph filter, and transmitted to their neighbor nodes, all nodes achieved average consensus. The simulation results show that the algorithm achieves the average consensus at the end, which can significantly improve the convergence speed and reduce the calculation amount compared with the existing methods.